{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Softmax exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "This exercise is analogous to the SVM exercise. You will:\n",
    "\n",
    "- implement a fully-vectorized **loss function** for the Softmax classifier\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** with numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clear previously loaded data.\n",
      "Train data shape:  (49000, 3073)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3073)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3073)\n",
      "Test labels shape:  (1000,)\n",
      "dev data shape:  (500, 3073)\n",
      "dev labels shape:  (500,)\n"
     ]
    }
   ],
   "source": [
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, num_dev=500):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the linear classifier. These are the same steps as we used for the\n",
    "    SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    \n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "    X_dev = X_train[mask]\n",
    "    y_dev = y_train[mask]\n",
    "    \n",
    "    # Preprocessing: reshape the image data into rows\n",
    "    X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "    X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "    X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "    X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "    \n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis = 0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "    X_dev -= mean_image\n",
    "    \n",
    "    # add bias dimension and transform into columns\n",
    "    X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "    X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "    X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "    X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev\n",
    "\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)\n",
    "print('dev labels shape: ', y_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Softmax Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/softmax.py**. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 2.365504\n",
      "sanity check: 2.302585\n"
     ]
    }
   ],
   "source": [
    "# First implement the naive softmax loss function with nested loops.\n",
    "# Open the file cs231n/classifiers/softmax.py and implement the\n",
    "# softmax_loss_naive function.\n",
    "\n",
    "from cs231n.classifiers.softmax import softmax_loss_naive\n",
    "import time\n",
    "\n",
    "# Generate a random softmax weight matrix and use it to compute the loss.\n",
    "W = np.random.randn(3073, 10) * 0.0001\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# As a rough sanity check, our loss should be something close to -log(0.1).\n",
    "print('loss: %f' % loss)\n",
    "print('sanity check: %f' % (-np.log(0.1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 1:\n",
    "Why do we expect our loss to be close to -log(0.1)? Explain briefly.**\n",
    "\n",
    "**Your answer:** *Fill this in*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -3.525914 analytic: -3.525914, relative error: 9.485269e-10\n",
      "numerical: -1.812702 analytic: -1.812701, relative error: 9.761496e-09\n",
      "numerical: 0.437253 analytic: 0.437253, relative error: 5.292899e-08\n",
      "numerical: 2.217169 analytic: 2.217169, relative error: 3.618750e-08\n",
      "numerical: 0.056363 analytic: 0.056363, relative error: 8.080633e-08\n",
      "numerical: -1.746537 analytic: -1.746537, relative error: 8.026695e-09\n",
      "numerical: -0.680875 analytic: -0.680875, relative error: 8.865688e-08\n",
      "numerical: 1.464192 analytic: 1.464192, relative error: 5.157207e-08\n",
      "numerical: 0.012770 analytic: 0.012770, relative error: 2.929834e-06\n",
      "numerical: -0.566365 analytic: -0.566365, relative error: 7.500952e-08\n",
      "numerical: -1.147877 analytic: -1.147877, relative error: 1.678616e-08\n",
      "numerical: 0.014122 analytic: 0.014122, relative error: 2.166956e-06\n",
      "numerical: 0.315914 analytic: 0.315914, relative error: 8.137685e-08\n",
      "numerical: -2.896706 analytic: -2.896706, relative error: 1.201028e-08\n",
      "numerical: -0.268961 analytic: -0.268961, relative error: 8.511794e-08\n",
      "numerical: 1.155289 analytic: 1.155289, relative error: 1.905000e-08\n",
      "numerical: 1.140792 analytic: 1.140792, relative error: 1.565884e-08\n",
      "numerical: -2.284472 analytic: -2.284472, relative error: 2.717186e-08\n",
      "numerical: 1.180741 analytic: 1.180741, relative error: 2.164230e-08\n",
      "numerical: 1.393131 analytic: 1.393130, relative error: 4.874470e-08\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of softmax_loss_naive and implement a (naive)\n",
    "# version of the gradient that uses nested loops.\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# As we did for the SVM, use numeric gradient checking as a debugging tool.\n",
    "# The numeric gradient should be close to the analytic gradient.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad, 10)\n",
    "\n",
    "# similar to SVM case, do another gradient check with regularization\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 3073)\n",
      "naive loss: 2.365504e+00 computed in 0.091000s\n",
      "[[ -68.64189796  -46.98173469  -73.47391837 ...,  -24.86195918\n",
      "   -53.39957143    1.        ]\n",
      " [  98.35810204   99.01826531  105.52608163 ...,  -48.86195918\n",
      "   -41.39957143    1.        ]\n",
      " [ -44.64189796  -64.98173469  -83.47391837 ...,   43.13804082\n",
      "    19.60042857    1.        ]\n",
      " ..., \n",
      " [  59.35810204   58.01826531   60.52608163 ...,   82.13804082\n",
      "    90.60042857    1.        ]\n",
      " [ 111.35810204  111.01826531  110.52608163 ...,    3.13804082\n",
      "    21.60042857    1.        ]\n",
      " [  97.35810204   98.01826531  112.52608163 ...,   80.13804082\n",
      "    89.60042857    1.        ]]\n",
      "vectorized loss: 2.365504e+00 computed in 0.005000s\n",
      "Loss difference: 0.000000\n",
      "Gradient difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Now that we have a naive implementation of the softmax loss function and its gradient,\n",
    "# implement a vectorized version in softmax_loss_vectorized.\n",
    "# The two versions should compute the same results, but the vectorized version should be\n",
    "# much faster.\n",
    "tic = time.time()\n",
    "print(X_dev.shape)\n",
    "loss_naive, grad_naive = softmax_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.softmax import softmax_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, grad_vectorized = softmax_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# As we did for the SVM, we use the Frobenius norm to compare the two versions\n",
    "# of the gradient.\n",
    "grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('Loss difference: %f' % np.abs(loss_naive - loss_vectorized))\n",
    "print('Gradient difference: %f' % grad_difference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-33-ffa49740998b>, line 26)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-33-ffa49740998b>\"\u001b[1;36m, line \u001b[1;32m26\u001b[0m\n\u001b[1;33m    for lr, reg in sorted(results):\u001b[0m\n\u001b[1;37m                                   ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of over 0.35 on the validation set.\n",
    "from cs231n.classifiers import Softmax\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_softmax = None\n",
    "learning_rates = [1e-7, 5e-7]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained softmax classifer in best_softmax.                          #\n",
    "################################################################################\n",
    "for lr in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        \n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "softmax on raw pixels final test set accuracy: 0.103000\n"
     ]
    }
   ],
   "source": [
    "# evaluate on test set\n",
    "# Evaluate the best softmax on test set\n",
    "y_test_pred = best_softmax.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('softmax on raw pixels final test set accuracy: %f' % (test_accuracy, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question** - *True or False*\n",
    "\n",
    "It's possible to add a new datapoint to a training set that would leave the SVM loss unchanged, but this is not the case with the Softmax classifier loss.\n",
    "\n",
    "*Your answer*:\n",
    "\n",
    "*Your explanation*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fT9NZkenRFO08bHKF7Y7lwwIP046XZf+6TLWn8iwL2oCrMmHUrSo+4UK7NpTg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wAAAg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\nCIIg2IC8TAmCIAiCINjAfwEvyMu3178bJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x80d0a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class\n",
    "w = best_softmax.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "    \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
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   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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